Artificial Intelligence for Management

The recommended Basic Knowledge (BK) is as follows:

• BK1. Knowledge of descriptive and inferential statistics, namely measures of central tendency and dispersion, probability distributions, correlation, estimation, and hypothesis testing.
• BK2. Basic linear algebra, including vectors, matrices, and solving linear systems, sufficient to understand data representation and transformation.
• BK3. Data manipulation and preparation, including importing data in common formats (CSV, Excel, or equivalents), data cleaning, handling missing values, variable transformation, and use of data tables.
• BK4. Data visualization and exploratory analysis, including construction and interpretation of tables, histograms, scatter plots, bar charts, and box plots.
• BK5. Technical reading in English, sufficient to understand software documentation, introductory scientific articles, and bibliography in the field.

The course unit is based on theoretical-practical classes. The teaching methods (TM) to be used are balanced between traditional and active.

The teaching methods (TM) to be used are balanced between traditional and active and are as follows:

• TM1. Presentation of content by the teacher: Compatible with learning objectives 1 to 7. Structured presentation of the concepts, models, architectures and techniques of Artificial Intelligence for Management, using examples, demonstrations and guided discussion, promoting the understanding of the scientific foundations of the course unit.
• TM2. Testing the content learned by the students: Compatible with learning objectives 1 to 7. Carrying out exercises, small challenges and moments of verification of learning throughout the classes, allowing the consolidation of knowledge, identification of difficulties and providing continuous feedback to students.
• TM3. Project-based learning: Compatible with learning objective 8. Development, preferably in groups, of an Artificial Intelligence for Management project, applied to a specific organization or domain, based on a set of real data, promoting the integration of acquired knowledge and the application of the methodology appropriate to the development of the project.
  TM4. Problem solving by students: Compatible with learning objectives 4 to 8. The development of the project will encourage students to solve problems, namely those related to knowledge representation, data preparation, modeling, inference and model evaluation, using appropriate computational tools.
• TM5. Interaction and sharing of ideas by students: Compatible with learning objective 8. The development of the project will encourage the promotion of interaction between students through the discussion of solutions, presentation of ideas, comparative analysis of approaches and sharing of experiences, fostering collaborative learning and the ability to communicate technically.
• TM6. Development of critical thinking by students: Compatible with learning objectives 7 and 8. The development of the project will encourage the critical analysis of models, techniques and results obtained, encouraging students to evaluate limitations, justify methodological choices, compare alternatives and propose well-founded improvements for different problems and application contexts.
• TM7. Research conducted by students: Compatible with learning objectives 4 to 8. Research and analysis of scientific articles, technical documentation and case studies on Artificial Intelligence for Management, integrating the state of the art into the development of the project and promoting autonomous learning.

The main learning objectives (LO) are as follows:

• LO1. To know the principles of artificial intelligence
• LO2. To know the principles of knowledge representation and inference
• LO3. To understand the operating principles of expert systems
• LO4. To know the main tasks and activities of artificial intelligence
• LO5. To know the main techniques and algorithms of artificial intelligence
• LO6. To know some tools and technologies and know how to use some of them
• LO7. To know how to evaluate the quality of solutions and know how to validate these solutions
• LO8. To know how to apply some of the main concepts and approaches learned in a practical project

The main Competencies to be Developed (CD) are as follows:

• CD1. Ability to formulate questions that can be answered by an artificial intelligence project
• CD2. Ability to frame an artificial intelligence project and analyze its feasibility
• CD3. Skill in proposing and creating models suitable for concrete problems and challenges
• CD4. Aptitude in understanding existing data from various sources and preparing new data to support models
• CD5. Ability to analyze the created models from the perspective of an organization’s business or a specific topic
• CD6. Ability to propose alternative approaches, whether related to data, models, or tools

• P1. Introduction to Artificial Intelligence
  • 1.1 History of Artificial Intelligence
  • 1.2 Principles of Artificial Intelligence, Machine Learning, and Deep Learning
  • 1.3 Weak, Strong, and Superintelligence Artificial Intelligence
  • 1.4 Knowledge Discovery in Databases and Data Mining
• P2. Knowledge and Inference
• P3. Expert Systems
• P4. Main Tasks and Activities
  • 4.1 Predictive (or Supervised) Activities
  • 4.2 Descriptive (or Unsupervised) Activities
  • 4.3 Prescriptive Activities
• P5. Main Techniques and Algorithms
  • 5.1 Decision Tree Induction
  • 5.2 Artificial Neural Networks
  • 5.3 Genetic Algorithms
  • 5.4 Rule Induction
  • 5.5 ​​Fuzzy Sets
  • 5.6 Bayesian Networks
  • 5.7 Other Techniques and Algorithms
• P6. Tools and Technologies
• P7. Solution Quality and Validation

Students can choose between two assessment methods: continuous assessment or examination.

A) Continuous Assessment

• MA1. Continuous Assessment of the 1st Test (AC1F) (30%): No consultation allowed, except for a brief formula sheet provided with the assignment. Subject to the timely submission of the 1st part of the project and a minimum of 2/3 attendance in class. The use of a calculator is permitted, but only non-programmable models. Minimum grade of 7 points. Mandatory.
• MA2. Continuous Assessment of the 2nd Test (AC1F) (30%): No consultation allowed, except for a brief formula sheet provided with the assignment. Subject to the timely submission of the 2nd part of the project and a minimum of 2/3 attendance in class. The use of a calculator is permitted, but only non-programmable models. Minimum grade of 7 points. Mandatory.
• MA3. Continuous Assessment of the Practical Project (ACPP) (40%): This involves a practical project proposal submitted by the student(s), according to a specific model, to be discussed and validated beforehand by the professor, otherwise it may not be accepted for evaluation. It may be carried out individually or in a group of up to 3 members. It must be presented in a session specifically scheduled for this purpose. It is subject to delivery within the established deadlines for both parts of the project. A minimum of 2/3 attendance is required. The grade awarded in the ACPP is guaranteed in any regular exam, provided it is in the same academic year. It is subject to delivery within the established deadlines for both parts of the project. It is mandatory.

Final Grade (FG) Formula (FG): CF = 30% x AC1F + 30% x AC2F + 40% x AAPP

B) Exam Assessment

• MA4. Final Written Exam (EFE) (60%): No consultation allowed, except for a brief formula sheet provided with the exam paper. It will take place during any of the regular exam periods. Taking the final written exam (FWE) component is mandatory during any regular exam period, and the grade for this component will not be carried over to any subsequent assessment period. The use of a calculator is permitted, but only a non-programmable model. Minimum grade of 7 points. Mandatory.
• MA5. Practical Project (PP) (40%): This involves a practical project proposal to be submitted by the student(s), according to a specific proposed model, to be discussed and validated beforehand by the professor, otherwise it may not be accepted for evaluation. The deadlines for submitting the PP in regular or resit periods will be defined at the appropriate time, but never before the Sunday following the last day of class. It may be carried out individually or in a group, with a maximum of 3 members. Each group must prepare and carry out a presentation and defense of the project. The presentation must be given by all authors and will take place on the same day and immediately after the final written exam (EFE), otherwise this component will not be counted. The ACPP does not preclude the student from being re-evaluated by exam in the project component; to do so, they must submit and defend a significantly improved version of the project on the dates established for the respective periods. The grade for the continuous assessment component of the practical project (ACPP) or the practical project component (PP) obtained in any assessment period will be valid for any subsequent assessment period, provided it occurs in the same academic year. It may be improved if the student takes an exam in a later assessment period. There are three possibilities for improving the grade given to a practical project: significant improvement of aspects of the project, preparation of a proposed article, or preparation of a poster in a pre-defined template. Mandatory.

Final Grade (FG) Formula: CF = 60% x EFE + 40% x PP

C) Regulations for the use of Generative Artificial Intelligence (GAI)

The use of Generative Artificial Intelligence (GAI) tools in this course is governed by the principles of academic integrity, transparency, and the critical use of technologies. GAI can be used as a tool to support learning, research, development, and project documentation, specifically for:

• exploratory research on concepts, methodologies, and technologies related to Artificial Intelligence for Management;
• support for structuring reports, technical documentation, and presentations;
• support for the implementation, documentation, debugging, and improvement of code or knowledge bases;
• support for the preliminary interpretation of results and identification of methodological alternatives.

The use of GAI does not replace the student's intellectual authorship, and the student is fully responsible for the scientific correctness, technical quality, justification of the choices made, and validation of all results presented.

In the practical project, whenever GAI tools are used, students must:

• identify the tools used;
• explicitly state how they were used;
• attach a list of the main prompts used, when relevant;
• indicate the changes, validations, or critical decisions made by the authors regarding the responses produced by the AI.

The submission of content produced entirely by AI systems without critical intervention, validation, and adaptation by students is not permitted. In the individual in-person assessment components (frequencies, exams, or oral tests), the use of AI tools is expressly prohibited. Failure to comply with these rules constitutes a breach of the principles of academic integrity and will be dealt with in accordance with the applicable institutional regulations.

NAO